mlsanityData to 7 Oct 2026

Adversarial Inverse Reinforcement Learning for Mean Field Games

arXiv 2104.14654 ↗First posted 29 Apr 2021cs.LGcited by 8

On the map: Offline RL and imitation learning from data → Inverse reinforcement learning

Abstract (arXiv)

Mean field games (MFGs) provide a mathematically tractable framework for modelling large-scale multi-agent systems by leveraging mean field theory to simplify interactions among agents. It enables applying inverse reinforcement learning (IRL) to predict behaviours of large populations by recovering reward signals from demonstrated behaviours. However, existing IRL methods for MFGs are powerless to reason about uncertainties in demonstrated behaviours of individual agents. This paper proposes a novel framework, Mean-Field Adversarial IRL (MF-AIRL), which is capable of tackling uncertainties in demonstrations. We build MF-AIRL upon maximum entropy IRL and a new equilibrium concept. We evaluate our approach on simulated tasks with imperfect demonstrations. Experimental results demonstrate the superiority of MF-AIRL over existing methods in reward recovery.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 14 of its 50 references that are in the map. First posted on arXiv 29 Apr 2021.

Builds on

14 of 50 references.

Closest ancestors · 2

Same direction, newest first

  1. 2019Meta-Inverse Reinforcement Learning with Probabilistic Context Variables20 Sep
  2. 2019Multi-Agent Adversarial Inverse Reinforcement Learning30 Jul

Other references · 12

  1. 2017Reinforcement Learning with Deep Energy-Based Policiescited by 659
  2. 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 400
  3. 2018Mean Field Multi-Agent Reinforcement Learningcited by 202
  4. 2018Multi-Agent Generative Adversarial Imitation Learningcited by 99
  5. 2021Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learningcited by 65
  6. 2020Entropy Regularization for Mean Field Games with Learningcited by 50
  7. 2019On the Convergence of Model Free Learning in Mean Field Gamescited by 45
  8. 2020Q-Learning in Regularized Mean-field Gamescited by 40
  9. 2015Learning in Mean Field Games: the Fictitious Playcited by 37
  10. 2016Markov-Nash Equilibria in Mean-Field Games with Discounted Costcited by 29
  11. 2020Multi Type Mean Field Reinforcement Learningcited by 25
  12. 2018Factorized Q-Learning for Large-Scale Multi-Agent Systemscited by 19

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 2; 2026: 2.

8 papers.

  1. 2023On Imitation in Mean-field Gamescited by 10
  2. 2024Maximum Causal Entropy IRL in Mean-Field Games and GNEP Framework for Forward RLcited by 4
  3. 2025Kernel Based Maximum Entropy Inverse Reinforcement Learning for Mean-Field Gamescited by 3
  4. 2024Inverse Concave-Utility Reinforcement Learning is Inverse Game Theorycited by 2
  5. 2025Trust Region Reward Optimization and Proximal Inverse Reward Optimization Algorithmcited by 1
  6. 2024Mean Field Correlated Imitation Learningcited by 0
  7. 2026Maximum Entropy Inverse Reinforcement Learning for Mean-Field Games with Average Rewardcited by 0
  8. 2026Neural Parameter Calibration for Finite-State Mean Field Gamescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022Individual-Level Inverse Reinforcement Learning for Mean Field Games
  2. 2025Meta-Inverse Reinforcement Learning for Mean Field Games via Probabilistic Context Variables
  3. 2024Enhancing Inverse Reinforcement Learning through Encoding Dynamic Information in Reward Shaping
  4. 2024Rethinking Adversarial Inverse Reinforcement Learning: Policy Imitation, Transferable Reward Recovery and Algebraic Equilibrium Proof
  5. 2024Scalable Offline Reinforcement Learning for Mean Field Games
  6. 2020oIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions
  7. 2018Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations
  8. 2020Scalable Multi-Agent Inverse Reinforcement Learning via Actor-Attention-Critic